DevOpsGPT
A DevOps-oriented system that turns natural-language requirements into working software.
- Source repo
- kuafuai/DevOpsGPT
- Stars
- ★ 6k
- Last updated
- 1mo ago
- License
- NOASSERTION
- Primary language
- HTML
- FA score
- Insufficient evidence
At a glance
- How it runs
- Works with
- Platform-specificOpenAI API
- Cost
- Free software; you pay for model usage
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- A development team that needs an initial implementation from business requirements written in natural language.
- Not a fit if
- Teams expecting automatic understanding of existing project code; the current version cannot
- Complex projects needing precise requirement and interface documentation
- Source review
- Insufficient evidence 8 safety controls not found
What does this agent do, and when should you use it?
DevOpsGPT combines a GPT language model with DevOps tools for software-development automation. Its browser-based service guides users through requirement clarification, then produces interface documentation and generated code in ./workspace. The documented workflow also includes developer refinement, continuous integration, testing, and release to a target environment. It can run from source or as the kuafuai/devopsgpt:latest Docker image, with the service normally available at http://127.0.0.1:8080. The README describes existing-project analysis and pseudocode generation, but its limitations section says the current version cannot automatically understand existing project code.
A user submits and clarifies requirements through the browser interface. DevOpsGPT generates interface documentation and writes generated code to ./workspace; the documented downstream flow is developer refinement, continuous integration and testing with DevOps tools, and deployment of a software version to a target environment. For a source deployment, copy env.yaml.tpl to env.yaml, add a GPT Token, and run sh run.sh on Linux or macOS, or run run.bat on Windows. For Docker, mount workspace and env.yaml, expose ports 8080 and 8081, and run kuafuai/devopsgpt:latest.
- A development team that needs an initial implementation from business requirements written in natural language.
- A backend team that wants generated interface documentation before engineers complete API design and implementation.
- An individual developer who wants to follow a browser-guided requirement workflow and inspect generated code in ./workspace.
- A team with Docker that wants to trial a requirement-to-code workflow in a containerized deployment.
- A DevOps team planning to place generated code into its existing integration, testing, and release process.
How do you install or deploy this agent?
Source deployment: prepare SQLite and Python 3.7 or later; download a released version or clone the code; copy env.yaml.tpl to env.yaml; edit env.yaml and add a GPT Token; run sh run.sh on Linux or macOS, or run run.bat on Windows. Docker deployment: run mkdir -p workspace; copy env.yaml.tpl into the current directory as env.yaml and add a GPT Token; then run:
docker run -it \
-v$PWD/workspace:/app/workspace \
-v$PWD/env.yaml:/app/env.yaml \
-p8080:8080 -p8081:8081 kuafuai/devopsgpt:latestHow do you use this agent?
After startup, open the address shown in the logs; the default is http://127.0.0.1:8080. Before using the service, configure a GPT Token in env.yaml. Follow the page guidance to submit and clarify requirements and complete the requirement-development flow. DevOpsGPT produces interface documentation and writes generated code to ./workspace. Inspect that workspace after generation; the README describes developers refining the resulting functionality, then using DevOps tools for continuous integration and testing before releasing a software version to the target environment.
What are this agent's strengths and limitations?
- Documents a workflow spanning requirement clarification, interface documentation, code generation, continuous integration, and release rather than code generation alone.
- Offers both source execution and a Docker image, with documented mounts and ports.
- Places generated code in ./workspace, giving engineers a concrete handoff point for review and refinement.
- It requires a GPT Token, and the README warns that OpenAI API token usage can be costly.
- Source execution depends on Python 3.7+ and SQLite; container execution additionally requires Docker.
- The README states that requirement and interface-documentation generation may be insufficiently precise in complex scenarios.
- The current version cannot automatically understand existing project code, despite documentation of an existing-project-oriented workflow.
- It is provided as an experimental, as-is application; users assume responsibility for failures, data loss, and compliance.
How does this agent compare with similar options?
The README lists Auto-GPT, gpt-engineer, and LangChain as reference projects, without feature or performance comparisons.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| DevOpsGPT This agent | Insufficient evidence | Self-hosted serviceFree + model costs | ★ 6k | 1mo ago | HTML | OpenAI API |
| Claw-Empire Agent Office | 72 · Some gaps | CLIFree + model costs | ★ 1.4k | 7mo ago | TypeScript | Codex · Claude Code · OpenAI API · Claude API |
| Zeroshot Verification Loop | 63 · Some gaps | CLIFree + model costs | ★ 2.1k | today | Rust | Codex · Claude Code |
| Agents Observe | 55 · Major gaps | Agent plugin / skillFree | ★ 714 | 2mo ago | TypeScript | Claude Code |
How does FollowAgents rate this agent?
Why each dimension lost points
Insufficient evidence: The repository does not provide explicit implementation or documentation for permission management, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. All trust-related criteria are unsupported, hence scored 0.
Insufficient evidence: No explicit evidence for self-consistency, dependency availability, or failure messages. The README describes a workflow but lacks details on error handling or dependency management. Hence scored 0.
Insufficient evidence: The README describes target audience and scenarios but lacks detailed capability boundaries, trigger precision, or environment fit. Hence scored 0.
Insufficient evidence: The README provides installation instructions and known limitations, but lacks explicit information architecture, naming stability, examples/FAQ, versioning/changelog, or maintenance responsibility. License exists but is custom, and no changelog is provided. Hence scored 0.
Insufficient evidence: The README claims efficiency improvements but provides no concrete evidence of output usability, marginal value, or cost-benefit. Hence scored 0.
Insufficient evidence: Claims in the README lack traceability, cross-source corroboration, or separation of facts and inferences. Hence scored 0.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- The repository lacks details on permission management, data flow transparency, or sensitive data handling; assess risks before use.
- Dependencies include unpinned versions and no security audit information is provided.
- The license is custom and may restrict commercial use; read carefully.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
-
Release notes excerpt
What's Changed - refactor(*): introduce service provider switching functionality @bdoycn - fix(subtask): Fixed incorrect response language @booboosui - feat(workspace): Support Git @booboosui - feat(ci): add gitlab ci support @booboosui - feat(cd): supports cloud service deployment @booboosui - feat(*): Supports docker startup @inceabdullah - fix(git): add git package to fix [Errno 2] No such file or directory: 'git' @inceabdullah - chore(doc): Add Japanese README @eltociear Full Changelog: https://github.com/kuafuai/DevOpsGPT/compare/v0.6.20...v0.6.21View on GitHub ↗ -
Release notes excerpt
What's Changed - The first release. Contributors @booboosui @yakeJiang @ericxinwu @richardkelly2014 @sonicrangView on GitHub ↗